Cerebrospinal fluid-based clinical-radiomics model for predicting treatment prognosis of acute ischemic stroke.

Background: Early alterations in cerebrospinal fluid (CSF) may play a critical role in the progression of acute ischemic stroke (AIS). This study aimed to develop a CSF-based clinical-radiomics model for predicting the functional outcomes in AIS patients following intravenous thrombolysis (IVT). Methods: This study included 308 AIS patients within 6 h of onset, divided into a training set (n=246) and a hold-out test set (n=62). Functional outcome was assessed at discharge using the modified Rankin Scale (mRS), dichotomized into good (mRS ≤2) and poor (mRS >2) outcomes. CSF regions were automatically segmented on non-contrast computed tomography images, and radiomics features were extracted. After feature selection was sequentially performed using minimum redundancy maximum relevance algorithm followed by least absolute shrinkage and selection operator, the radiomics signature model was constructed. Clinical features were selected through univariate and multivariate logistic regressions and subsequently integrated with radiomics features to develop combined models. Three machine learning classifiers, including Nu Support Vector Classification (NuSVC), logistic regression, and random forest, were trained and tested for prognostic prediction. Model performance was evaluated using receiver operating characteristic curve analysis, decision curve analysis (DCA), and calibration curves, with internal validation via five-fold cross-validation. Results: Among the 308 patients included, 155 patients had a good outcome and 153 had a poor outcome. A total of 1,874 radiomics features were extracted, among which 21 were selected for the radiomics model. Three clinical features were also identified for the clinical model. The combined clinical-radiomics model significantly outperformed single-modality models across all classifiers. The NuSVC-based combined model achieved the best performance, with an area under the curve (AUC) of 0.870 [95% confidence interval (CI): 0.850-0.889] in cross-validation and 0.893 (95% CI: 0.817-0.968) in the test cohort. DCA further confirmed the superior clinical utility of the combined model. Conclusions: This study demonstrates the value of CSF radiomics signatures in predicting functional outcomes in AIS patients after IVT. The CSF-based combined model exhibited strong prognostic performance and clinical utility, highlighting its potential for supporting treatment decision-making.